FER models have reached excellent performance on controlled datasets: on CK+, Frame Attention networks proposed by [1]} have attained 99.7% accuracy. However, FER models perform less well with in-the-wild datasets: the state-of-the-art (SOTA) accuracy on the FER+ dataset with cleaned and updated labels is 89.75% reached by a PSR model on seven expressions [2]}. SOTA on the AffectNet dataset, in contrast, is only 65.74% for seven of the eight included emotion categories [3]}. Given that FER systems in practical use, such as humanoid robots and surveillance systems, will not be fed with regular illuminations, frontal head position and exemplary expressions, it is important to improve FER accuracy on such in-the-wild datasets.
